Bilateral Hermite Radial Basis Functions for Contour-based Volume Segmentation

back to top

Takashi Ijiri, Shin Yoshizawa, Yu Sato, Masaaki Ito, Hideo Yokota

Abstract

In this paper, we propose a novel contour-based volume image segmentation technique. Our technique is based on an implicit surface reconstruction strategy , whereby a signed scalar field is generated from user-specified contours. The key idea is to compute the scalar field in a joint spatial-range domain (i.e., bilateral domain) and resample its values on an image manifold. We introduce a new formulation of Hermite radial basis function (HRBF) interpolation to obtain the scalar field in the bilateral domain. Incontrast to previous implic it methods, bilateral HRBF (B-HRBF) generates a segmentation boundary that passes through all contours, fits high-contrast image edges if they exist, and has a smooth shape in blurred areas of images. We also propose an acceler ation scheme for computing B-HRBF to support a real-time and intuitive segmentation interface. In our experiments, we achieved high-quality segmentation results for regions of interest with high-contrast edges and blurred boundaries.

Information

Materials

Paper(Definitive version) paper(preprint) talk slide (ppt) talk slide (pdf) software (Japanese only)


Takashi Ijiri, Shin Yoshizawa, Yu Sato, Masaaki Ito, and Hideo Yokota.: Bilateral Hermite Radial Basis Functions for Contour-based Volume Segmentation. Computer Graphics Forum, Vol. 32, Issue 2, pp. 123-132, 2013. EUROGRAPHICS 2013.

@Article{Ijiri_EG13,
    author   = {Takashi Ijiri and Shin Yoshizawa and Yu Sato and Masaaki Ito and Hideo Yokota},
    title    = {{Bilateral Hermite Radial Basis Functions for Contour-based Volume Segmentation}},
    journal  = {Computer Graphics Forum},
    year     = {2013},
    volume   = {32},
    number   = {2},
    pages    = {123-132},
    note     = {Proc. of EUROGRAPHICS'13}
}

Implementation tips


For our Bilateral Hermit Radial Basis Function, ...(3),
we used the following two kernels in our experiments:
,
.
The both φ1 and φ2 provide convincing results.

The gradient of these kernels are computed as follows;



The Hessian matrices of these kernels are computed as follows;
             
    

                              
    


where is a k-dimensional vector and is a k x k identity matrix.
We provide our implementation in our software (VoTracer).
(we will provide an English document and some example volumes in Oct 2014.)

back to top